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Description: Machine Learning with WEKA: An Introduction （讲义）
关于数据挖掘和机器学习的.-Machine Learning with WEKA : An Introduction (s) on the Data Mining and Machine Learning.
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Size: 10908 |
Author: 黄波 |
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Description: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
一个可以实现多种方法分类的软件，利用各个
对象的属性。决策树，距离、密度等-Weka is a collection of machine learning al gorithms for data mining tasks. The algorithms can either be applied directly to a dataset or ca lled from your own Java code. Weka contains tool 's for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for d eveloping new machine learning schemes. can be a real Categories are various methods of software, using all the attributes of objects. Decision Tree, distance, density, etc.
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Size: 15446626 |
Author: 马何坛 |
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Description: Machine Learning with WEKA: An Introduction （讲义）
关于数据挖掘和机器学习的.-Machine Learning with WEKA : An Introduction (s) on the Data Mining and Machine Learning.
Platform: |
Size: 10240 |
Author: 黄波 |
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Description: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
一个可以实现多种方法分类的软件，利用各个
对象的属性。决策树，距离、密度等-Weka is a collection of machine learning al gorithms for data mining tasks. The algorithms can either be applied directly to a dataset or ca lled from your own Java code. Weka contains tool 's for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for d eveloping new machine learning schemes. can be a real Categories are various methods of software, using all the attributes of objects. Decision Tree, distance, density, etc.
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Size: 15446016 |
Author: 马何坛 |
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Description: Weka，一个数据挖掘工具。功能包括：分类、聚类和关联规则等等。这是该开源软件的源代码，版本为3.5.7-Weka, a data mining tool. Features include: classification, clustering and association rules, etc.. This is the open source software source code, version 3.5.7
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Size: 4790272 |
Author: Jess |
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Description: This example demonstrates how to use WEKA s SVMs classifier in Matlab.
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Size: 50176 |
Author: huglepzh |
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Description: 基于局部搜索能力强、收敛速度快的特点,首先初始化一个没有子种群的全局种群,再在全局种群中采用迭代搜索,并对其中的个体进行聚类,当聚类簇中的个体数目达到规定的最小规模时形成一个子种群,然后在各子种群中进行迭代搜索并重新进行聚类,从而提高进化过程中种群的多样性,增强算法跳出局部最优的能力.该算法基于weka，用于weka拓展功能，需要 weka算法包支持。-Based on the local search ability, the characteristics of fast convergence, first initialize a sub-population of the overall population, then the overall population in the iterative search, and clustering of the individuals, when the clustering of individual cluster achieve the required minimum number of the scale of the formation of a subset of the population, and then in the sub-populations in the iterative search and re-clustering to improve the evolutionary process of population diversity, enhancement algorithm' s ability to jump out of local optimum.
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Size: 5120 |
Author: zhangrui |
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Description: WEKA 3-5-5 Explorer为数据挖掘软件，文件为用户指南-WEKA 3-5-5 Explorer for data mining software, documents User' s Guide
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Size: 549888 |
Author: Daniel |
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Description: jBNC is a Java toolkit for training, testing, and applying
Bayesian Network Classifiers. Implemented classifiers have been
shown to perform well in a variety of artificial intelligence,
machine learning, and data mining applications.
jBNC is primarily intended as a library for creation of Bayesian
Classifier networks. Several algorithms for creation of networks
are included.
To aid testing the quality of classifier network a couple of simple
command line tools for training and testing are included, see section
TOOLS for more details.
There is also a separate package called jBNC-WEKA that integrates
jBNC with WEKA (Waikato Environment for Knowledge Analysis
http://www.cs.waikato.ac.nz/~ml). jBNC-WEKA allows creation of
jBNC classifiers from within WEKA, in particular, using WEKA s
graphical user interface. For more info see jBNC homepage.
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Size: 828416 |
Author: sakthivel |
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Description: 一篇人工智能领域的论文，作者利用weka环境选择合适的分类器对星际争霸2的录像数据进行玩家行为建模，旨在提高AI对玩家水平的判断。文章难度不高，对于初学人工智能和模式识别的很有启发性。-An artificial intelligence paper, the authors use weka environment select the appropriate classification for StarCraft 2‘s video player behavior data modeling, aimed at raising the level of AI players to judge. Articles difficulty is not high, for the beginner artificial intelligence and pattern recognition is instructive.
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Size: 317440 |
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Description: Using Weka s feature selection algorithm
X, the features on current trunk, each colum is a feature vector on all
instances, and each row is a part of the instance
Y, the label of instances, in single column form: 1 2 3 4 5 ...
a.E = weka.attributeSelection.SymmetricalUncertAttributeSetEval
a.S = weka.attributeSelection.FCBFSearch -D false -T -1.7976931348623157E308 -N -1- Using Weka s feature selection algorithm
X, the features on current trunk, each colum is a feature vector on all
instances, and each row is a part of the instance
Y, the label of instances, in single column form: 1 2 3 4 5 ...
a.E = weka.attributeSelection.SymmetricalUncertAttributeSetEval
a.S = weka.attributeSelection.FCBFSearch -D false -T -1.7976931348623157E308 -N -1
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Size: 1024 |
Author: alaa |
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